1. Data (geo)science project

2. Exploring your data

3. Modelling

4. Wrangling (Load, Tidy and Transform)

Each section:
5–10 minutes of introduction
5–10 minutes of live coding and questions

# Install PAGES from GitHub:
# install.packages("devtools") 
devtools::install_github("MartinSchobben/PAGES", build_vignettes = TRUE)

• webpage: https://martinschobben.github.io/PAGES/index.html
  
• github repo: https://github.com/MartinSchobben/PAGES

Load PAGES with library.

library(PAGES)

The tidyverse universe: opinionated collection of R packages designed for data science.

• ggplot2: A modular graphing tool for custom solutions
• dplyr: Getting data into the right format
• readr: Opinionated import tools for rectangular data.
• tidyr: Cast data into a consistent tidy format

• In stratigraphy we look at variations in the value of a variable through time (or time series).

• The time unit is measured in height or depth, and can sometimes be calibrated for absolute time (age model).

• R package PAGES contains the lazy load data: bonenburg (geochemistry) and kuhjoch (palynology). For more information on the dataset use ? (?bonenburg).

• Beginner: Emphasis on R objects and output.
• Expert: Emphasis on scripts and functions.

RStudio projects

1. A clear directory and file structure with meta-data to describe data. Raw data should be read-only and backed-up.

2. R script with a clear documentation of all steps involved.

3. Publish all aspects of this workflow along with your paper.

Pipe: %>%

• intermediate steps

kuhjoch_grps <- group_by(kuhjoch_long, type)
kuhjoch_sum <- summarise(kuhjoch_grps, mean(count))

• function composition

kuhjoch_sum <- summarise(group_by(kuhjoch_long, type), mean(count))

• pipe

kuhjoch_sum <- group_by(kuhjoch_long, type) %>%
  summarise(mean(count))

“The simple graph has brought more information to the data analyst’s mind than any other device.” — John Tukey

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(
     mapping = aes(<MAPPINGS>),
     stat = <STAT>, 
     position = <POSITION>,
     orientation = <ORIENTATION>
  ) +
  <FACET_FUNCTION>

Scatterplot: maps each observation to a horizontal and vertical position and the geom represents this as a point.

ggplot(data = bonenburg) +
  geom_point(mapping = aes(x = del13Ctoc, y = height)) 

The colour, shape and linetype can also be used to map additional variables. Here I use the stratigraphy (categorical) as an additional variable.

ggplot(data = bonenburg) +
  geom_point(mapping = aes(x = del13Ctoc, y = height, colour = strat))

Internal (statistical) transformation of <DATA>.

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(
    mapping = aes(<MAPPINGS>),
    stat = <STAT>,
    position = <POSITION>,
    orientation = <ORIENTATION>
    ) +
  <FACET_FUNCTION>

ggplot(data = bonenburg) + 
  geom_boxplot(mapping = aes(y = strat, x = del13Ctoc), stat = "boxplot")

ggplot(data = bonenburg) +
  geom_boxplot(mapping = aes(y = reorder(strat, height), x = del13Ctoc))

The facets split data according to a categorical variable.

ggplot(data = bonenburg_long) +
  geom_point(mapping = aes(x = value, y = height)) +
  facet_grid(cols = vars(measurement), scales = "free_x") 

• Discern patterns (or signals) from noise.

• Exploration, not confirmation or formal inference!

ggplot(data = bonenburg_cross, mapping = aes(x = value, y = del13Ctoc)) +
  geom_point(aes(colour = strat)) +
  facet_wrap(facets = vars(measurement), scales = "free") 

ggplot(data = bonenburg_cross, mapping = aes(x = value, y = del13Ctoc)) +
  geom_point(aes(colour = strat)) +
  geom_smooth() +
  facet_wrap(facets = vars(measurement), scales = "free") 

This was a very simple, exploratory analysis of the data.

Fitting models:

• Additive components and interactions (multivariate regression).
  
• Residual diagnostics to validate the assumptions of the model.
  
• Correlation structure (time series have a correlation between time steps).
  
• Grouping structure (mixed effect models).

Further reading:

• Peter Dalgaard 2008 Introduction to statistics with R
  
• John Fox & Sanford Weisberg 2018 An R companion to applied regression
  
• Alain Zuur et al. 2008 Mixed Effects Models and Extensions in Ecology with R

Load your data into R with the readr package:

• read_csv(): comma separated (CSV) files
• read_tsv(): tab separated files
• read_delim(): general delimited files

Description on website: “In many cases, these functions will just work!”

Reversely, you can also write back to several file formats with write_*

PAGES_example()

## [1] "bonenburg_cross.csv" "bonenburg_long.csv"  "bonenburg_raw.csv"  
## [4] "bonenburg_tidy.csv"  "bonenburg.csv"       "kuhjoch_long.csv"   
## [7] "kuhjoch_raw.csv"     "kuhjoch.csv"

read_csv(PAGES_example("bonenburg_raw.csv"))

## # A tibble: 108 x 13
##    SampleID Height CaCO3    TN del13Ctoc TOCcfb `Al2O3 (%)` `Na2O (%)` `K2O (%)`
##       <dbl>  <dbl> <dbl> <dbl>     <dbl>  <dbl>       <dbl>      <dbl>     <dbl>
##  1        0   3.01 13.3   0.06     -27.5   1.16        15.6       0.62      3.25
##  2       60   3.56  2.67 NA        -25.5   0.27        13.1       1.27      3.33
##  3      100   3.95  3.84  0.07     -27.3   0.96        17.4       0.55      3.54
##  4      150   4.43  5.86  0.07     -27     1.25        17.6       0.44      3.79
##  5      200   4.94 12.8   0.07     -27.8   1.52        16.4       0.48      3.73
##  6      250   5.25  3.34  0.09     -27.6   2.45        14.6       0.61      3.42
##  7      275   5.68  9.91  0.06     -27     1.19        17.3       0.44      4.18
##  8      300   5.92 NA    NA         NA    NA           15.5       0.46      3.74
##  9      300   6.16 22.2   0.06     -27.1   1.21        NA        NA        NA   
## 10      350   6.41 20.5   0.06     -27.5   1.14        15.1       0.51      3.92
## # … with 98 more rows, and 4 more variables: Strat <chr>, Strat2 <chr>,
## #   Section <chr>, Reference <chr>

There are three interrelated rules which make a dataset tidy:

Create, rename, reorder variable and summarise with tidyverse dplyr.

XRF oxides and normalization with elemental ratios.

mutate(
  bonenburg_tidy,
  # oxide correction
  Al_pc = Al2O3_pc * with(marelac::atomicweight, 2 * Al / (2 * Al + 3 * O)),
  Na_pc = Na2O_pc * with(marelac::atomicweight, Na / (Na + 2 * O)),
  K_pc = K2O_pc * with(marelac::atomicweight, K / (K + 2 * O)),
  .keep = "unused"
  ) %>%
  # normalization with Al and rename
  mutate(
    across(c(Na_pc, K_pc), ~.x / Al_pc, .names = "{gsub(\"pc\", \"\", .col)}Al"),
    .keep = "unused"
    )

Steps of a data (geo)science project:

1. Create an RProject and organised file structure.
   
2. Load your data with readr.
   
3. Wrangle:
   • Tidy your data with tidyr.
     
   • Create new variables, rename and reorder with dplyr.
     
4. Visualize data with ggplot2.
   
5. Model and observe patterns with ggplot2 and other tools.
   
6. Reiterate steps 3–5.
   
7. Publish or present results along with documentation of intermediate steps.

Data
lazy load data: bonenburg and kuhjoch as well as the long formats: bonenburg_long and kuhjoch_long.

Raw data
PAGES_example()

Examples
- project: vignette("project", package = "PAGES)
- explore: vignette("explore", package = "PAGES)
- model: vignette("model", package = "PAGES)
- wrangle: vignette("wrangle", package = "PAGES)

Slides
render_slides()